Adsorption Structures, Vibrational Raman Spectra and Chemical Binding Properties of Thioglycolic Acid on Cu(111) Surfaces: A DFT Study.

Copper is widely used in everyday life and industrial production because of its good electrical and thermal conductivity. To overcome copper oxidation and maintain its good physical properties, small organic molecules adsorbed on the surface of copper make a passivated layer to further avoid copper corrosion. In this work, we have investigated thioglycolic acid (TGA, another name is mercaptoacetic acid) adsorbed on copper surfaces by using density functional theory (DFT) calculations and a periodical slab model. We first get five stable adsorption structures, and the binding interaction between TGA and Cu(111) surfaces by using density of states (DOS), indicating that the most stable configuration adopts a triple-end binding model. Then, we analyze the vibrational Raman spectra of TGA adsorbed on the Cu(111) surface and make vibrational assignments according to the vibrational vectors. Finally, we explore the temperature effect of the thermodynamically Gibbs free energy of TGA on the Cu(111) surface and the antioxidant ability of the small organic molecular layer of copper oxidation on the copper surface. Our calculated results further provide evidences to interpret the stability of adsorption structures and antioxidant properties of copper.

Machine-learning-assisted performance improvements for multi-resonance thermally activated delayed fluorescence molecules.

With favorable colour purity, multi-resonance thermally activated delayed fluorescence (MR-TADF) molecules exhibit enormous potential in high-definition displays. Due to the relatively small chemical space of MR-TADF molecules, it is challenging to improve molecular performance through domain-specific expertise alone. To address this problem, we focused on optimizing the classic molecule, DABNA-1, using machine learning (ML). Molecular morphing operations were initially employed to generate the adjacent chemical space of DABNA-1. Subsequently, a machine learning model was trained with a limited database and used to predict the properties throughout the generated chemical space. It was confirmed that the top 100 molecules suggested by machine learning present excellent electronic structures, characterized by small reorganization energy and singlet-triplet energy gaps. Our results indicate that the improvement in electronic structures can be elucidated through the view of the molecular orbital (MO). The results also reveal that the top 5 molecules present weaker vibronic peaks of the emission spectrum, demonstrating higher colour purity when compared to DABNA-1. Notably, the M2 molecule presents a high RISC rate, indicating its promising future as a high-efficiency MR-TADF molecule. Our machine-learning-assisted approach facilitates the rapid optimization of classical molecules, addressing a crucial requirement within the organic optoelectronic materials community.

Seasonal Variation, Fractional Isolation and Nanoencapsulation of Antioxidant Compounds of Indian Blackberry (Syzygium cumini).

Indian blackberry (Syzygium cumini L.) is an evergreen tree in the Myrtaceae family. It is used in traditional medicine due to its significant bioactivities and presence of polyphenols with antioxidant activities. The present study describes the effect of seasonal variations on Indian blackberry leaf essential oil yield and chemical composition, production of fractions from essential oil using high vacuum fractional distillation and slow cooling to low temperature (-50 °C) under vacuum, and bioactivities of the essential oil, fractions, and nanoparticles. The results show that Indian blackberry essential oil yield was higher in spring season as compared to winter season. Indian blackberry essential oil fractionation processes were effective in separating and concentrating compounds with desired bioactivities. The bioactivities shown by magnesium nanoparticles were comparatively higher than barium nanoparticles.